Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit.
Artificial intelligence assistance raised general radiologists' aneurysm detection performance on CT angiography
Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit.
We aimed to validate a clinically available artificial intelligence (artificial intelligence) model to assist general radiologists in the detection of intracranial aneurysm (intracranial aneurysm) in a multi-reader multi-case (multi-reader multi-case) study, and to explore its performance in routine clinical settings.
Cohort 1, comprising gold-standard consecutive CT angiography cases, was used in an multi-reader multi-case study involving six board-certified general radiologists.
Cohort 1 consisted of 131 CT angiography cases, while Cohort 2 included 515 CT angiography cases.
In the artificial intelligence-based first-reader study, 60.4% of the CT angiography cases were identified as negative by the artificial intelligence, with a high negative predictive value of 0.994 (95%CI: 0.977-0.999).
This study highlights the clinical utility of a high-performance artificial intelligence model in detecting IAs, significantly improving general radiologists' diagnostic performance with the potential to reduce their workload in routine clinical practice.
The algorithmic audit offers insights to guide the development and validation of future artificial intelligence models.